Changes in myoplasmic Ca<sup>2+</sup>during fatigue differ between FDB fibers, between glibenclamide-exposed and Kir6.2<sup>-/-</sup>fibers and are further modulated by verapamil
Bibliographic record
Abstract
One objective of this study was to document how individual FDB muscle fibers depend on the myoprotection of KATP channels during fatigue. Verapamil, a CaV1.1 channel blocker, prevents large increases in unstimulated force during fatigue in KATP-channel-deficient muscles. A second objective was to determine if verapamil reduces unstimulated [Ca(2+)]i in KATP-channel-deficient fibers. We measured changes in myoplasmic [Ca(2+)] ([Ca(2+)]i) using two KATP-channel-deficient models: (1) a pharmacological approach exposing fibers to glibenclamide, a channel blocker, and (2) a genetic approach using fibers from null mice for the Kir6.2 gene. Fatigue was elicited with one tetanic contraction every sec for 3 min. For all conditions, large differences in fatigue kinetics were observed from fibers which had greater tetanic [Ca(2+)]i at the end than at the beginning of fatigue to fibers which eventually completely failed to release Ca(2+) upon stimulation. Compared to control conditions, KATP-channel-deficient fibers had a greater proportion of fiber with large decreases in tetanic [Ca(2+)]i, fade and complete failure to release Ca(2+) upon stimulation. There was, however, a group of KATP-channel-deficient fibers that had similar fatigue kinetics to those of the most fatigue-resistant control fibers. For the first time, differences in fatigue kinetics were observed between Kir6.2(-/-) and glibenclamide-exposed muscle fibers. Verapamil significantly reduced unstimulated and tetanic [Ca(2+)]i. It is concluded that not all fibers are dependent on the myoprotection of KATP channels and that the decrease in unstimulated force by verapamil reported in a previous studies in glibenclamide-exposed fibers is due to a reduction in Ca(2+) load by reducing Ca(2+) influx through CaV1.1 channels between and during contractions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".